What is conversational AI?

Conversational AI, explained

Conversational AI is an umbrella term, not a product. It describes the capability to carry on a conversation: understand what a person means, keep track of context across turns, decide what to do next and respond in natural language.

Older conversational systems split this into separate parts: an intent classifier to work out what the user wanted, entity extraction to pull out details such as an order number, a dialogue manager to choose the next step, and templates to phrase the reply. Large language models collapse much of this into one model that can read the whole conversation and write the next message, though production systems still add retrieval, guardrails and action logic around the model.

In customer support, conversational AI shows up as website chat agents, messaging bots and voice systems in contact centres. The business value comes from handling repeat questions at any hour and collecting context before a person picks up.

The hard parts are not the conversation itself but accuracy, knowing when to stop, and connecting safely to real systems such as order data.

Common mistake

How SuperBot relates

SuperBot applies conversational AI to website and store support: it answers in 35 languages from your own content, keeps the full transcript, and passes the conversation to your team when it should.

• SuperBot features
• How SuperBot works

Related terms

• ChatbotA chatbot is software that holds a text conversation with a person, usually in a website widget or messaging app. Rule-based chatbots follow scripted flows and keyword triggers; AI chatbots use language models to understand free-form questions and generate replies. In customer support, chatbots answer common questions and route the rest to people.
• AI customer support agentAn AI customer support agent is software that uses a large language model to answer customer questions in natural language, grounded in a business's own content, and can take bounded actions such as looking up an order, capturing a lead or handing the conversation to a human when it cannot help.
• Intent detectionIntent detection is identifying what a customer is trying to achieve from their message, such as tracking an order, requesting a refund or asking about pricing, and labelling it as a category. Chatbots use it to choose a response or route a conversation, and support teams use intent reports to see what customers ask about most.
• Retrieval-augmented generation (RAG)Retrieval-augmented generation (RAG) is a technique in which an AI system first searches a trusted knowledge source for passages relevant to a question, then gives those passages to a language model to write the answer. In customer support, RAG keeps replies tied to the business's current policies and help content instead of the model's general training data.
• Sentiment analysisSentiment analysis is the automated detection of the emotional tone in text, typically classified as positive, neutral or negative. In customer support, it is applied to messages and conversations to spot frustrated customers, track how people feel about topics over time, and flag conversations for a human to review, without waiting for a survey response.